Approximate nearest neighbor search for low-dimensional queries
SIAM Journal on Computing, ISSN: 0097-5397, Vol: 42, Issue: 1, Page: 138-159
2013
- 8Citations
- 1Usage
- 11Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations8
- Citation Indexes8
- CrossRef1
- Usage1
- Abstract Views1
- Captures11
- Readers11
- 11
Article Description
We study the approximate nearest neighbor problem for metric spaces where the query points are constrained to lie on a subspace of low doubling dimension, while the data is high dimensional. We show that this problem can be solved efficiently despite the high dimensionality of the data. © 2013 Society for Industrial and Applied Mathematics.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84876259620&origin=inward; http://dx.doi.org/10.1137/110852711; http://epubs.siam.org/doi/10.1137/110852711; http://epubs.siam.org/doi/pdf/10.1137/110852711; https://digitalcommons.memphis.edu/facpubs/2524; https://digitalcommons.memphis.edu/cgi/viewcontent.cgi?article=3523&context=facpubs
Society for Industrial & Applied Mathematics (SIAM)
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